Adaptive Resonance Theory: Self-Organizing Networks for Stable Learning, Recognition, and Prediction

Adaptive Resonance Theory (ART) is a neural theory of human and primate information processing and of adaptive pattern recognition and prediction for technology. Biological applications to attentive learning of visual recognition categories by inferotemporal cortex and hippocampal system, medial temporal amnesia, corticogeniculate synchronization, auditory streaming, speech recognition, and eye movement control are noted. ARTMAP systems for technology integrate neural networks, fuzzy logic, and expert production systems to carry out both unsupervised and supervised learning. Fast and slow learning are both stable response to large non stationary databases. Match tracking search conjointly maximizes learned compression while minimizing predictive error. Spatial and temporal evidence accumulation improve accuracy in 3-D object recognition. Other applications are noted.Office of Naval Research (N00014-95-I-0657, N00014-95-1-0409, N00014-92-J-1309, N00014-92-J4015); National Science Foundation (IRI-94-1659

Cyclic creep analysis from elastic finite-element solutions

A uniaxial approach was developed for calculating cyclic creep and stress relaxation at the critical location of a structure subjected to cyclic thermomechanical loading. This approach was incorporated into a simplified analytical procedure for predicting the stress-strain history at a crack initiation site for life prediction purposes. An elastic finite-element solution for the problem was used as input for the simplified procedure. The creep analysis includes a self-adaptive time incrementing scheme. Cumulative creep is the sum of the initial creep, the recovery from the stress relaxation and the incremental creep. The simplified analysis was exercised for four cases involving a benchmark notched plate problem. Comparisons were made with elastic-plastic-creep solutions for these cases using the MARC nonlinear finite-element computer code

Education and Innovative Capabilities

This study investigates the role of capabilities, acquired through education and on the job learning, in innovation. It is argued that education enhances learning and innovation because it provides employees with communication and interaction skills, and, more importantly, with abilities to receive, understand and utilize relevant knowledge, and solve problems. These dynamic capabilities are one of the sources of innovation. A dataset of 333 Finnish manufacturing firms is used to estimate the factors that influence the probability of making product and process innovations, and incremental product improvements. The period of study is 1987-91. The estimations suggest that competences and skills acquired through education and work experience are important for innovation. Different types of innovation turn out to be affected by different competences. General level of education is important for product innovation. Technical skills are relevant for both innovation and incremental improvement of products, whereas firm-specific work experience comes into play with incremental product improvements and process innovation. However, process innovation seems to be determined mainly by firm size, instead of competences or industry-specific factors. This suggests that the life cycle stage may be related to the type of innovation undertaken. According to the estimations there are considerable lags involved with the effects of competences on innovation. However, longer time series would be needed to evaluate the underlying dynamics properly

SIRU development. Volume 1: System development

A complete description of the development and initial evaluation of the Strapdown Inertial Reference Unit (SIRU) system is reported. System development documents the system mechanization with the analytic formulation for fault detection and isolation processing structure; the hardware redundancy design and the individual modularity features; the computational structure and facilities; and the initial subsystem evaluation results

Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements

Background subtraction has been a fundamental and widely studied task in video analysis, with a wide range of applications in video surveillance, teleconferencing and 3D modeling. Recently, motivated by compressive imaging, background subtraction from compressive measurements (BSCM) is becoming an active research task in video surveillance. In this paper, we propose a novel tensor-based robust PCA (TenRPCA) approach for BSCM by decomposing video frames into backgrounds with spatial-temporal correlations and foregrounds with spatio-temporal continuity in a tensor framework. In this approach, we use 3D total variation (TV) to enhance the spatio-temporal continuity of foregrounds, and Tucker decomposition to model the spatio-temporal correlations of video background. Based on this idea, we design a basic tensor RPCA model over the video frames, dubbed as the holistic TenRPCA model (H-TenRPCA). To characterize the correlations among the groups of similar 3D patches of video background, we further design a patch-group-based tensor RPCA model (PG-TenRPCA) by joint tensor Tucker decompositions of 3D patch groups for modeling the video background. Efficient algorithms using alternating direction method of multipliers (ADMM) are developed to solve the proposed models. Extensive experiments on simulated and real-world videos demonstrate the superiority of the proposed approaches over the existing state-of-the-art approaches.Comment: To appear in IEEE TI

Fate and impact of organics in an immersed membrane bioreactor applied to brine denitrification and ion exchange regeneration

The application of membrane bioreactors (MBRs) to brine denitrification for ion exchange regeneration has been studied. The developed culture was capable of complete brine denitrification at 50 gNaCl.l−1. Denitrification reduced to c.60% and c.70% when salinity was respectively increased to 75 and 100 g.l−1, presumed to be due to reduced growth rate and the low imposed solids retention time (10 days). Polysaccharide secretion was not induced by stressed cells following salt shocking, implying that cell lysis did not occur. Fouling propensity, monitored by critical flux, was steady at 12–15 l.m−2.h−1 during salinity shocking and after brine recirculation, indicating that the system was stable following perturbation. Low molecular weight polysaccharide physically adsorbed onto the nitrate selective anion exchange resin during regeneration reducing exchange capacity by c.6.5% when operating up to complete exhaustion. However, based on a breakthrough threshold of 10 mgNO3−-N.l−1 the exchange capacity was comparative to that determined when using freshly produced brine for regeneration. It was concluded that a denitrification MBR was an appropriate technology for IEX spent brine reco